We all know that, right?

• The Semantic Web Artificial Intelligence on the Web
• It relies on centrally controlled ontologies for “meaning”
  • as opposed to a democratic, bottom–up control of terms
• One has to add metadata to all Web pages, convert all relational databases, and XML data to use the Semantic Web
• It is just an ugly application of XML
• One has to learn formal logic, knowledge representation techniques, description logic, etc
• It is, essentially, an academic project, of no interest for industry
• …

WRONG!!!!

• The Semantic Web Artificial Intelligence on the Web
• It relies on centrally controlled ontologies for “meaning”
  • as opposed to a democratic, bottom–up control of terms
• One has to add metadata to all Web pages, convert all relational databases, and XML data to use the Semantic Web
• It is just an ugly application of XML
• One has to learn formal logic, knowledge representation techniques, description logic, etc
• It is, essentially, an academic project, of no interest for industry
• …

Goal of this presentation…

• There are lots of myths around the Semantic Web
• This presentation will try to de-mystify at least some of those…

Is the Semantic Web AI on the Web?

No!

So what is the Semantic Web?

• Humans can easily “connect the dots” when browsing the Web…
  • you disregard advertisements
  • you “know” (from the context) that this link is interesting and goes to my CV; whereas the that one is without interest
  • etc.
• … but machines can’t!
• The goal is to have a Web of Data to ensure smooth integration with data, too
• Let us see just some application examples…

Example: Automatic Airline Reservation

• Your automatic airline reservation
  • knows about your preferences
  • builds up knowledge base using your past
  • can combine the local knowledge with remote services:
    • airline preferences
    • dietary requirements
    • calendaring
    • etc
• It communicates with remote information (i.e., on the Web!)
• (M. Dertouzos: The Unfinished Revolution)

Example: data(base) integration

• Databases are very different in structure, in content
• Lots of applications require managing several databases
  • after company mergers
  • combination of administrative data for e-Government
  • biochemical, genetic, pharmaceutical research
  • etc.
• Most of these data are now on the Web (though not necessarily public yet)

Example: data integration in life sciences

And the problem is real

So what is the Semantic Web?

The Semantic Web is… the Web of Data

• It allows machines to “connect the dots”
• It provides a common framework to share data on the Web across application boundaries

And what is the relationship to AI?

• Some technologies in the Semantic Web has benefited from AI research and development (see later)
• Semantic Web has also brought some new concerns, problems, use cases to AI
• But AI has many many different problems that are not related to the Web at all (image understanding is a good example)

All right, but what is RDF then?

RDF

• For all applications listed above the issues are to create relations among resources on the Web and to interchange those data
• Pretty much like (hyper)links on the traditional web, except that:
  • there is no notion of “current” document; ie, relationship is between any two resources
  • a relationship must have a name: a link to my CV should be differentiated from a link to my Calendar
  • there is no attached user-interface action like for a hyperlink

RDF (cont.)

• RDF is a model for such relationships and interchange
  • to be a bit more techie: it is a model of (s p o) triplets with p naming the relationship between s and o
• URI-s are used as universal naming tools, including for properties (after all, “U” stands for “Universal”…)
• That is it (essentially)! Nothing very complex…

But isn’t RDF simply an (ugly) XML application?

RDF is a graph!

• As we already said: RDF is a set of relationships
• An (s,p,o) triple can be viewed as a labeled edge in a graph
  • i.e., a set of RDF statements is a directed, labeled graph
    • the nodes represent the resources that are bound
    • the labeled edges are the relationships with their names
• This set must be serialized for machines; this can be done into XML (using RDF/XML), or to other formats (Turtle, N-Triples, TriX, …)
• Think in terms of graphs, the rest is syntactic sugar!

A Simple RDF Example

<rdf:Description rdf:about="http://www.ivan-herman.net">
    <foaf:name>Ivan</foaf:name>
    <abc:myCalendar rdf:resource="http://…/myCalendar"/>
    <foaf:surname>Herman</foaf:surname>
</rdf:Description>

Yes, RDF/XML has its Problems

• RDF/XML was developed in the “prehistory” of XML
  • e.g., even namespaces did not exist!
• Coordination was not perfect, leading to problems
  • the syntax cannot be checked with XML DTD-s
  • XML Schemas are also a problem
  • encoding is verbose and complex (simplifications lead to confusions…)
• but there is too much legacy code to change it

Use, e.g., Turtle if you prefer…

<http://www.ivan-herman.net>

  foaf:firstName "Ivan";

  abc:myCalendar <http://.../myCalendar>;

  foaf:surname "Herman".

• Again: these are all just syntactic sugar!
• RDF environments often understand several serialization syntaxes
• In some cases, authoring tools hide the details anyway!

But what has RDF to do with data integration?

Consider this (simplified) bookstore data set

Export your data as a set of relations…

Add the data from another publisher…

Start merging…

Simple integration…

Note the role of URI-s!

• The URI-s made the merge possible
• URI-s ground RDF into the Web
• URI-s make this the Semantic Web

So what is then the role of ontologies and/or rules?

A possible short answer

• Ontologies/rules are there to help integration
• Let us come back to our example…

This is where we are…

Our merge is not complete yet…

• We “feel” that a:author and f:auteur should be the same
• But an automatic merge doest not know that!
• Let us add some extra information to the merged data:
  • a:author same as f:auteur
  • both identify a “Person”:
    • a term that a community has already defined (part of the “FOAF” terminology)
    • a “Person” is uniquely identified by his/her name and, say, homepage
    • it can be used as a “category” for certain type of resources
    • we can also identify, say, a:name with foaf:name

Better merge: richer queries are possible!

What we did: we used ontologies…

• We said:

• a:author same as f:auteur
• both identify a “Person”:
  • a term that a community has already defined
  • a “Person” is uniquely identified by his/her name and, say, homepage
  • it can be used as a “category” for certain type of resources
  • we can also identify, say, a:name with foaf:name

• These statements can be described in an ontology (or, alternatively, with rules)
• The ontology/rule serves as some sort of a “glue”

And then the merge may go on…

…and on…

…and on…

Is that surprising?

• Maybe but, in fact, no…
• What happened via automatic means is done all the time by the (human) users of the Web!
• The difference: a bit of extra rigor (eg, naming the relationships), extra information (eg, identifying relationships) and machines could do this, too

Important issue: “schema independence”

• The queries (ie, the application) sees the RDF data only (with references to “real” data)
• If the structure (“schema”) of the database changes, only the mapping to RDF has to be changed
  • this is a very local change
• Ie, the RDF layer is very robust vis-a-vis schema evolution (not only to schema differences)

You remember this statement?

• It relies on giant, centrally controlled ontologies for “meaning”

• Ontologies are usually developed by communities and they are to be shared
  • in fact, in our example, we used an ontology called “FOAF”

And this?

• One has to learn formal logic, knowledge representation techniques, description logic, etc, to understand the Semantic Web and be able to use it

• This “glue” does not have to be complex, it may be of a few lines only
  • “a little semantics can take you far…”

Tradeoffs

• What can be inferred depends on the level of additional knowledge (ie, “glue”) one adds to the original data
• More complex ontologies: more inference possibilities, but more complex reasoning procedures
• At present, W3C has defined a set of ontology languages (and is working on rules)
• An application may choose the complexity it wants

“One has to learn formal logic, knowledge representation techniques, description logic, etc”

Not really…

• Yes, the detailed semantics of RDFS, OWL Lite, etc, are based on knowledge representation algorithms
  • OWL-DL stands for “OWL Description Logic”; it is an embodiment of a Description Logic
• …but most users just have to use these
• It is just like SQL: the formal semantics is very complex, but 95% of the SQL users have never even looked at it!
• Developing and ontology may require more knowledge, but that is for a small percentage of users (and there are authoring tools to hide the details)

Where do the data and ontologies come from?

(Should we really expect the author to type in all this data?)

Pure RDF data: not always a solution…

• Creating large scale RDF data with an editor is possible, but does not really scale…
  • although it may be o.k. for small things like the “glue” in our example
• Even if it is around: adding RDF to, say, XHTML, is not always easy
  • there are number of disagreeable technical problems with, eg, validation
  • the only “clean” approach today is to link it via a meta header element

Data may be around already…

• Part of the (meta)data information is present in tools … but thrown away at output
  • e.g., a business chart can be generated by a tool…
  • …it “knows” the structure, the classification, etc. of the chart, but, usually, this information is lost
• storing it in web data would be easy!
• “SW-aware” tools are around (even if you do not know it…), though more would be good:
  • Photoshop CS stores metadata in RDF in, say, jpg files (referred to as XMP)
  • RSS 1.0 feeds are generated by (almost) all blogging systems (a huge amount of RDF data!)
  • …

Data may be extracted (a.k.a. “scraped”)

• Different tools, services, etc, come around every day:
  • get RDF data associated with images, for example:
    • service to get RDF from flickr images (see example)
    • service to get RDF from XMP (see example)
  • XSLT scripts to retrieve microformat based information from XHTML files
  • scripts to convert spreadsheets to RDF
  • etc
• Most of these tools are still individual “hacks”, but show a general tendency

Formalizing the scraper approach: GRDDL

• GRDDL formalizes the scraper approach. For example:

<html xmlns="http://www.w3.org/1999/">
  <head profile="http://www.w3.org/2003/g/data-view">
    <title>Some Document</title>
    <link rel="transformation" href="http:…/dc-extract.xsl"/>
    <meta name="DC.Subject" content="Some subject"/>      
    ...
  </head>
  ...
  <span class="date">2006-01-02</span>
  ...
</html>

• yields, by running the file through dc-extract.xsl

<rdf:Description rdf:about="…">
  <dc:subject>Some subject</dc:subject>
  <dc:date>2006-01-02</dc:date>
</rdf:Description>

GRDDL (cont)

• Somebody has to provide dc-extract.xsl and use its conventions (making use of the corresponding meta-s, class id-s, etc…)
• … but, by using the profile attribute, a client is instructed to find and run the transformation processor automatically
• A “bridge” to “microformats”
• A W3C Working Group has just started, with a recommendation planned in the 1st Quarter of 2007

Another Future Solution: RDFa

• RDFa (formerly known as RDF/A) extends XHTML by:
  • extending the link and meta elements to include children
  • defining general attributes to add metadata to any elements (a bit like the class in microformats, but via dedicated properties)
• It is very similar to microformats, but with more rigor:
  • it is a general framework (instead of an “agreement” on the meaning of, say, a class attribute value)
  • terminologies can be mixed more easily
• The W3C Working Group on SW Deployment has this on its charter

RDFa example

• For example

<div about="http://uri.to.newsitem">
  <span property="dc:date">March 23, 2004</span>
  <span property="dc:title">Rollers hit casino for £1.3m</span>
  By <span property="dc:creator">Steve Bird</span>. See
  <a href="http://www.a.b.c/d.avi" rel="dcmtype:MovingImage">
  also video footage</a>…
</div>

• yields, by running the file through a processor:

<http://uri.to.newsitem>
  dc:date             "March 23, 2004";
  dc:title            "Rollers hit casino for £1.3m;
  dc:creator          "Steve Bird";
  dcmtype:MovingImage <http://www.a.b.c/d.avi>.

Common in RDFa and GRDDL

• The user authors XHTML as usual
• The result is displayed as usual
• The author may add some annotations that leads to RDF

Linking to SQL

• A huge amount of data in Relational Databases
• Although tools exist, it is not feasible to convert that data into RDF
• Instead: SQL ⇋ RDF “bridges” are being developed:
  • a query to RDF data is transformed into SQL on-the-fly
  • the modalities are governed by small, local ontologies or rules
• An active area of development!

And for Ontologies?

• The hard work is to create the ontologies in general
  • requires a good knowledge of the area to be described
  • some communities have good expertise already (e.g., librarians)
  • OWL is just a tool to formalize ontologies
• Large scale ontologies are often developed in a community process
  • leading to versioning issues, too
  • OWL includes predicates for versioning, deprecation, “same-ness”, …
• There is also R&D in generating them from a corpus of data
  • still mostly a research subject
• Sharing ontologies may be vital in the process

There are already ontologies around…

• Lots of ontologies registered at Schemaweb
• DAML ontology library has several hundreds of ontologies
• Ontologies are being developed by various communities:
  • medical domain (e.g., the US Cancer Institute’s Cancer Ontology, the Gene Ontology, the BioPax Molecular Pathway Ontology, …)
  • cultural heritage domain (e.g., CIDOC reference model and ontology)
  • OWL representation of (English) Wordnet
  • eBusiness ontology for products and services: eClassOwl
  • …
• Use existing ontologies when you can!

“Core” vocabularies

• A number of public “core” vocabularies evolve to be used by applications, e.g.:
  • SKOS Core: about knowledge systems
  • Dublin Core: about information resources, digital libraries, with extensions for rights, permissions, digital right management
  • FOAF: about people and their organizations
  • DOAP: on the descriptions of software projects
  • MusicBrainz: on the description of CDs, music tracks, …
  • SIOC: Semantically-Interlinked Online Communities
  • …

A mix of ontologies (a life science example)…

How do I extract triplets from and RDF Graph? Ie: how do I query an RDF Graph?

Querying RDF graphs

• RDBS model has a query language: SQL
• RDF (graph) model needs a query language: SPARQL
  • query graph patterns

Simple SPARQL Example

SELECT ?cat ?val # note: not ?x!

WHERE { ?x rdf:value ?val. ?x category ?cat }

• Returns: [["Total Members",100],["Total Members",200],…,["Full Members",10],…]

Other SPARQL features

• Define optional patterns
• Limit the number of returned results; remove duplicates, sort them,…
• Add functional constraints to pattern matching
• Return a full subgraph (instead of a list of bound variables)
• Use datatypes and/or language tags when matching a pattern
• SPARQL is not yet finalized, but will become a Recommendation (hopefully) in 2^nd Quarter of 2007
  • but there are a number of implementations already!

SPARQL as a federating tool

Isn't This Research Only?

(or: does this have any industrial relevance whatsoever?)

Not any more…

• Lots of tools are available. Are listed on W3C’s wiki:
  • RDF programming environment for 14+ languages, including C, C++, Python, Java, Javascript, Ruby, PHP,… (no Cobol or Ada yet !)
  • 13+ Triple Stores, ie, database systems to store (sometimes huge!) datasets
  • a number programming environments (in Java, Prolog, …) include OWL reasoners
  • there are also stand-alone reasoners (downloadable or on the Web)
  • etc
• Some of the tools are Open Source, some are not; some are very mature, some are not : it is the usual picture of software tools, nothing special any more!
• Anybody can start developing RDF-based applications today

Not any more… (cont)

• SW has indeed a strong foundation in research results
• But remember:
  • (1) the Web was born at CERN…
  • (2) …was first picked up by high energy physicists…
  • (3) …then by academia at large…
  • (4) …then by small businesses and start-ups…
  • (5) “big business” came only later!
• network effect kicked in early…
• Semantic Web is now at #4, and moving to #5!

Some RDF deployment areas (cont)

• Some deployment areas are already very active: Health Care and Life Sciences, Digital Libraries, Defense
  • also at W3C, in the form of an Interest Group for HCLS
• Others are coming to the fore: eGovernment, energy sector (oil industry), financial services, …

The “corporate” landscape is moving

• See, for example, the Semantic Technology Conference series
  • not a scientific conference, but commercial people making real money!
  • speakers in 2006: from IBM, Cisco, BellSouth, GE, Walt Disney, Nokia, Oracle, …
  • not all referring to Semantic Web (eg, RDF, OWL, …) but semantics in general
  • but they might come around!
• Major companies offer (or will offer) Semantic Web tools or systems using Semantic Web: Adobe, Oracle, IBM, HP, Software AG, webMethods, Northrop Gruman, Altova, …
• “Corporate Semantic Web” listed as major technology by Gartner in 2006

Applications are not always very complex…

• Eg: simple semantic annotations of patients’ data greatly enhances communications among doctors
• What is needed: some simple ontologies, an RDFa/microformat type editing environment
• Simple but powerful!

Data integration

• Data integration comes to the fore as one of the SW Application areas
• Very important for large application areas (life sciences, energy sector, eGovernment, financial institutions), as well as everyday applications (eg, reconciliation of calendar data)
• Life sciences example:
  • data in different labs…
  • data aimed at scientists, managers, clinical trial participants…
  • large scale public ontologies (genes, proteins, antibodies, …)
  • different formats (databases, spreadsheets, XML data, XHTML pages)
  • etc

Example: antibodies demo

• Scenario: find the known antibodies for a protein in a specific species
• Combine (“scrape”…) three different data sources
  • “Entrez protein sequence” from National Center for Biotechnology Information; conversion to RDF
  • “Antibody Directory” from Alzheimer Research Forum; scraping RDF from HTML
  • “Taxonomy information” from Wikispecies; use XSLT to extract RDF from XHTML
• Use SPARQL as an integration tool (see also demo online)

There has been lots of R&D

• Boeing, MITRE Corp., Elsevier, EU Projects like Sculpteur and Artiste, national projects like MuseoSuomi, DartGrid, …
• Developments are under way at various places in the area

Portals

• Vodafone's Live Mobile Portal
  • search application (e.g. ringtone, game, picture) using RDF
    • page views per download decreased 50%
    • ringtone up 20% in 2 months
• Sun’s SwordFish: public queries for support, handbooks, etc, go through an internal RDF engine for White Paper Collections and System Handbook collections
• Nokia has a somewhat similar support portal
• Harper’s Online magazine links items together via an internal ontology

Improved Search via Ontology: GoPubMed

• Improved search on top of pubmed.org
  • search results are ranked using the specialized ontologies
  • extra search terms are generated and terms are highlighted
• Importance of domain specific ontologies for search improvement

Summary

• The Semantic Web is not as complex as people believe
• The Semantic Web does not require huge investments before seeing its value
• The Semantic Web is not only for geeks…

Thank you for your attention!

(Slides are available from: http://www.w3.org/2006/Talks/0927-Berlin-IH/)